One method of creating machine generated pronunciation of names is to use grapheme-to-phoneme (G2P) tools to convert the letter string of a given name into phoneme sequences. These G2P tools are either rule based or apply statistical models that are learned from human created dictionaries. They are especially error prone for names, given the wide variety of pronunciations for a given spelling of a name depending on both the named person and the person speaking the name.
Another method of creating machine generated pronunciation of names is to ask a set of users to speak all personal names for which a pronunciation is to be learned. The resulting audio samples are then converted to phoneme sequences by applying a phonetic recognizer. A variation of this approach is to pick from multiple pronunciations that were created with a G2P tool by picking the G2P pronunciation(s) that are closest to the pronunciation(s) used by the speakers. This data-driven method is capable of yielding more accurate pronunciations than the first method, but it requires users to explicitly provide speech samples for all names.